import asyncio import multiprocessing as mp import os import socket import time from argparse import Namespace from collections.abc import AsyncGenerator, Iterable, Mapping, Sequence from concurrent.futures import ThreadPoolExecutor, as_completed from typing import Any import torch # External library imports (vLLM) import vllm.envs as envs from vllm.config import VllmConfig from vllm.engine.protocol import EngineClient from vllm.inputs import PromptType from vllm.inputs.preprocess import InputPreprocessor from vllm.logger import init_logger from vllm.lora.request import LoRARequest from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry from vllm.plugins.io_processors import get_io_processor from vllm.sampling_params import SamplingParams from vllm.tokenizers import TokenizerLike, init_tokenizer_from_config from vllm.tracing import init_tracer from vllm.transformers_utils.config import maybe_register_config_serialize_by_value from vllm.usage.usage_lib import UsageContext from vllm.utils.func_utils import deprecate_kwargs from vllm.v1.engine.async_llm import AsyncLLM from vllm.v1.engine.core_client import EngineCoreClient from vllm.v1.engine.exceptions import EngineDeadError from vllm.v1.executor.abstract import Executor from vllm.v1.metrics.loggers import StatLoggerFactory, StatLoggerManager # Internal imports (our code) from vllm_omni.config import OmniModelConfig from vllm_omni.distributed.omni_connectors import ( get_stage_connector_config, initialize_orchestrator_connectors, ) from vllm_omni.distributed.omni_connectors.adapter import try_send_via_connector from vllm_omni.distributed.ray_utils.utils import ( create_placement_group, get_ray_queue_class, try_close_ray, ) from vllm_omni.engine.arg_utils import AsyncOmniEngineArgs from vllm_omni.engine.input_processor import OmniInputProcessor from vllm_omni.engine.output_processor import MultimodalOutputProcessor from vllm_omni.entrypoints.client_request_state import ClientRequestState from vllm_omni.entrypoints.log_utils import ( OrchestratorMetrics, configure_orchestrator_logger, init_stats_paths, remove_old_logs, ) from vllm_omni.entrypoints.omni_stage import OmniStage from vllm_omni.entrypoints.stage_utils import maybe_load_from_ipc as _load from vllm_omni.entrypoints.utils import ( get_final_stage_id_for_e2e, load_stage_configs_from_model, load_stage_configs_from_yaml, resolve_model_config_path, ) from vllm_omni.outputs import OmniRequestOutput logger = init_logger(__name__) class AsyncOmni(EngineClient): """Async entry point for vLLM-Omni inference. This class provides an asynchronous interface for running multi-modal comprehension and generation models. It orchestrates multiple stages in a pipeline, where each stage runs in a separate process. Designed for use with async/await patterns and streaming generation. Args: model: Model name or path to load cli_args: Namespace object containing command-line arguments. Expected attributes include: - stage_configs_path: Optional path to YAML file containing stage configurations. If None, configurations are loaded from the model. - log_stats: Whether to enable statistics logging - log_file: Optional path prefix for log files. If provided, logs will be written to files with stage-specific suffixes. - init_sleep_seconds: Number of seconds to sleep between starting each stage process during initialization - shm_threshold_bytes: Threshold in bytes for using shared memory for IPC. Objects larger than this threshold will use shared memory. - batch_timeout: Timeout in seconds for batching requests within a stage - init_timeout: Timeout in seconds for waiting for all stages to initialize **kwargs: Additional keyword arguments passed to stage engines Example: >>> async_llm = AsyncOmni(model="Qwen/Qwen2.5-Omni-7B", cli_args=args) >>> async for output in async_llm.generate( ... prompt="Hello", ... request_id="req-1", ... sampling_params_list=[SamplingParams(), SamplingParams()] ... ): ... print(output) """ def __init__( self, model: str, cli_args: Namespace, **kwargs: Any, ): self.worker_backend = getattr(cli_args, "worker_backend", "multi_process") self.ray_address = getattr(cli_args, "ray_address", "") self._ray_pg = None self.batch_timeout = cli_args.batch_timeout self._enable_stats: bool = bool(cli_args.log_stats) # Pause / resume state for async RL workflows. self._pause_cond = asyncio.Condition() self._paused = False base_engine_args = AsyncOmniEngineArgs.from_cli_args(cli_args).__dict__.copy() if cli_args.stage_configs_path is None: self.config_path = resolve_model_config_path(model) self.stage_configs = load_stage_configs_from_model(model, base_engine_args) else: self.config_path = cli_args.stage_configs_path self.stage_configs = load_stage_configs_from_yaml(cli_args.stage_configs_path, base_engine_args) shm_threshold_bytes = cli_args.shm_threshold_bytes self.output_handler: asyncio.Task | None = None self.request_states: dict[str, ClientRequestState] = {} # request_id -> state # Initialize connectors self.omni_transfer_config, self.connectors = initialize_orchestrator_connectors( self.config_path, worker_backend=self.worker_backend, shm_threshold_bytes=shm_threshold_bytes ) self.stage_list: list[OmniStage] = [] self.default_sampling_params_list: list[SamplingParams] = [] # Optional file handler for orchestrator self._log_file = cli_args.log_file if self._log_file: remove_old_logs(self._log_file, len(self.stage_configs)) configure_orchestrator_logger(logger, self._log_file) self._stats_file, self._overall_stats_file = init_stats_paths(self._enable_stats, self._log_file) self._initialize_stages(model, cli_args.init_sleep_seconds, cli_args.shm_threshold_bytes, cli_args.init_timeout) # Here is a duplicated init for the first stage to get the tokenizer, # input_processor, and io_processor # TODO: Refactor the code to avoid the duplicated init, we need to pass # the tokenizer, input_processor, and io_processor back here from the # first stage. vllm_config = self.stage_list[0].vllm_config self.tokenizer = init_tokenizer_from_config(model_config=vllm_config.model_config) self.input_processor = OmniInputProcessor( vllm_config=vllm_config, tokenizer=self.tokenizer, ) self.model_config = vllm_config.model_config self.io_processor = get_io_processor( vllm_config, self.model_config.io_processor_plugin, ) def _initialize_stages( self, model: str, init_sleep_seconds: int, shm_threshold_bytes: int, init_timeout: int, ) -> None: self.stage_list: list[OmniStage] = [] # Build OmniStage instances in parallel, preserve original order def _build_stage(idx_cfg: tuple[int, Any]) -> tuple[int, OmniStage]: idx, cfg = idx_cfg return idx, OmniStage(cfg) with ThreadPoolExecutor(max_workers=min(len(self.stage_configs), max(1, os.cpu_count() or 1))) as executor: futures = [executor.submit(_build_stage, (idx, cfg)) for idx, cfg in enumerate(self.stage_configs)] results: list[tuple[int, OmniStage]] = [] for fut in as_completed(futures): results.append(fut.result()) results.sort(key=lambda x: x[0]) self.stage_list = [st for _, st in results] self.default_sampling_params_list = [st.default_sampling_params for st in self.stage_list] self.output_modalities = [st.final_output_type for st in self.stage_list] logger.debug("[Orchestrator] Loaded %d stages", len(self.stage_list)) if self.worker_backend == "ray": self._queue_cls = get_ray_queue_class() else: self._ctx = mp.get_context("spawn") self._queue_cls = lambda: self._ctx.Queue(maxsize=0) self._stage_in_queues: list[mp.Queue] = [] self._stage_out_queues: list[mp.Queue] = [] self._init_sleep_seconds = max(0, int(init_sleep_seconds)) self._shm_threshold_bytes = max(0, int(shm_threshold_bytes)) self._start_stages(model) # Wait for all stages to report readiness before seeding self._stages_ready: set[int] = set() self._wait_for_stages_ready(timeout=init_timeout) def _start_stages(self, model: str) -> None: if self.worker_backend == "ray": # Initialize Ray Cluster self._ray_pg = create_placement_group( number_of_stages=len(self.stage_list), address=self.ray_address, strategy="PACK" ) for stage_id, stage in enumerate(self.stage_list): # Use unbounded queues to avoid deadlock when seeding many requests in_q = self._queue_cls() out_q = self._queue_cls() self._stage_in_queues.append(in_q) self._stage_out_queues.append(out_q) # Attach queues and start Stage-owned worker process stage.attach_queues(in_q, out_q) # Build connectors config for this stage stage_connectors_config = get_stage_connector_config( self.omni_transfer_config, stage_id, ) stage.init_stage_worker( model, is_async=True, log_file=self._log_file, shm_threshold_bytes=self._shm_threshold_bytes, ctx=self._ctx if self.worker_backend != "ray" else None, batch_timeout=self.batch_timeout, connectors_config=stage_connectors_config, worker_backend=self.worker_backend, ray_placement_group=self._ray_pg, ) logger.debug("[Orchestrator] Stage-%s process started", stage_id) time.sleep(self._init_sleep_seconds) def close(self) -> None: """Close all stage processes and clean up resources. Sends shutdown signals to all stage input queues and stops all stage worker processes. This method should be called when done using the AsyncOmni instance. """ for q in self._stage_in_queues: try: q.put_nowait(None) except Exception as e: logger.warning( "[Orchestrator] Failed to send shutdown signal to \ stage input queue: %s", e, ) for stage in self.stage_list: try: stage.stop_stage_worker() except Exception as e: logger.warning("[Orchestrator] Failed to stop stage worker: %s", e) if self.output_handler is not None: self.output_handler.cancel() self.output_handler = None try_close_ray(self._ray_pg) def __del__(self) -> None: # best-effort try: self.close() except Exception as e: logger.debug("[Orchestrator] __del__ close() raised: %s", e, exc_info=True) def shutdown(self): """Shutdown, cleaning up the background proc and IPC. Alias for close() method. Cleans up all stage processes and inter-process communication resources. """ try: self.close() except Exception as e: logger.debug("[Orchestrator] __del__ close() raised: %s", e, exc_info=True) async def generate( self, prompt: PromptType, request_id: str, sampling_params_list: SamplingParams | Sequence[SamplingParams] | None = None, output_modalities: list[str] | None = None, lora_request: LoRARequest | None = None, trace_headers: Mapping[str, str] | None = None, priority: int = 0, data_parallel_rank: int | None = None, ) -> AsyncGenerator[OmniRequestOutput, None]: """Generate outputs for the given prompt asynchronously. Processes the prompt through all stages in the pipeline and yields outputs as they become available. Each stage uses its corresponding sampling parameters from the sampling_params_list. Args: prompt: Prompt to process. Can be a text string, token IDs, or multimodal prompt. request_id: Unique identifier for this request sampling_params_list: List of SamplingParams, one for each stage. Must have the same length as the number of stages. If None, uses default sampling params for each stage. lora_request: Optional LoRA adapter request for this generation trace_headers: Optional tracing headers for observability priority: Request priority (higher values processed first) data_parallel_rank: Optional data parallel rank for distributed inference Yields: OmniRequestOutput objects as they are produced by each stage. Each output contains the stage_id, final_output_type, and the request_output from that stage. Raises: ValueError: If sampling_params_list has incorrect length. """ # Wait until generation is resumed if the engine is paused. async with self._pause_cond: await self._pause_cond.wait_for(lambda: not self._paused) logger.debug("[Orchestrator] generate() called") # Start output handler on the first call to generate() self._run_output_handler() if sampling_params_list is None: sampling_params_list = self.default_sampling_params_list if len(sampling_params_list) != len(self.stage_list): raise ValueError( f"Expected {len(self.stage_list)} sampling params, \ got {len(sampling_params_list)}" ) # Orchestrator keeps stage objects for input derivation num_stages = len(self.stage_list) # Track per-request start time for end-to-end timing _req_start_ts: dict[int, float] = {} _wall_start_ts: float = time.time() # _last_finish_ts: float = _wall_start_ts # Determine the final stage for E2E stats (highest stage_id with # final_output=True; fallback to last stage) final_stage_id_for_e2e = get_final_stage_id_for_e2e(output_modalities, self.output_modalities, self.stage_list) # Metrics/aggregation helper metrics = OrchestratorMetrics( num_stages, self._enable_stats, self._stats_file, self._overall_stats_file, _wall_start_ts, ) # Seed stage-0 queue with all requests logger.debug("[Orchestrator] Seeding request into stage-0") req_state = ClientRequestState(request_id) self.request_states[request_id] = req_state # Mark first input time for stage-0 metrics.stage_first_ts[0] = metrics.stage_first_ts[0] or time.time() sp0: SamplingParams = sampling_params_list[0] # type: ignore[index] task = { "request_id": request_id, "engine_inputs": prompt, "sampling_params": sp0, } self.stage_list[0].submit(task) _req_start_ts[request_id] = time.time() logger.debug("[Orchestrator] Enqueued request %s to stage-0", request_id) logger.debug("[Orchestrator] Entering scheduling loop: stages=%d", num_stages) for stage_id, stage in enumerate(self.stage_list[: final_stage_id_for_e2e + 1]): result = await req_state.queue.get() assert stage_id == req_state.stage_id req_id = result.get("request_id") if "error" in result: logger.error( "Stage %s error on request %s: %s", stage_id, req_id, result["error"], ) raise RuntimeError(result) # Request Finished due to error engine_outputs = _load(result, obj_key="engine_outputs", shm_key="engine_outputs_shm") # Mark last output time for this stage whenever we receive outputs metrics.stage_last_ts[stage_id] = max(metrics.stage_last_ts[stage_id] or 0.0, time.time()) try: _m = result.get("metrics") if _m is not None: metrics.on_stage_metrics(stage_id, req_id, _m) except Exception as e: logger.exception( "[Orchestrator] Failed to process metrics for stage %s, \ req %s: %s", stage_id, req_id, e, ) logger.debug( "[Orchestrator] Stage-%s completed request %s; \ forwarding or finalizing", stage_id, req_id, ) stage.set_engine_outputs(engine_outputs) if getattr(stage, "final_output", False): logger.debug( "[Orchestrator] Request %s finalized at stage-%s", req_id, stage_id, ) # End-to-end timing and time-per-token for final output # (only once per request at the designated final stage) try: rid_key = str(req_id) if stage_id == final_stage_id_for_e2e and rid_key not in metrics.e2e_done: metrics.on_finalize_request( stage_id, req_id, engine_outputs, _req_start_ts.get(req_id, _wall_start_ts), ) except Exception as e: logger.exception( "[Orchestrator] Finalize request handling error for \ req %s at stage %s: %s", req_id, stage_id, e, ) if isinstance(engine_outputs, list): engine_outputs = engine_outputs[0] yield OmniRequestOutput( stage_id=stage_id, final_output_type=stage.final_output_type, request_output=engine_outputs, ) # Forward to next stage if there is one next_stage_id = stage_id + 1 if next_stage_id <= final_stage_id_for_e2e: next_stage: OmniStage = self.stage_list[next_stage_id] next_inputs = next_stage.process_engine_inputs(self.stage_list, prompt) sp_next: SamplingParams = sampling_params_list[next_stage_id] # Check if we have a connector for this edge connector_key = (str(stage_id), str(next_stage_id)) connector = self.connectors.get(connector_key) sent_via_connector = False if connector: sent_via_connector = try_send_via_connector( connector=connector, stage_id=stage_id, next_stage_id=next_stage_id, req_id=req_id, next_inputs=next_inputs, sampling_params=sp_next, original_prompt=prompt, next_stage_queue_submit_fn=self.stage_list[next_stage_id].submit, metrics=metrics, ) if not sent_via_connector: # Fallback logic removed as we now enforce connector usage. # If no connector is found or send fails, we log an error and raise, # because continuing would cause the request to be silently dropped # and the orchestrator to hang waiting for completion. error_msg = ( f"[Orchestrator] Failed to send request {req_id} to stage-{next_stage_id} via connector. " "Configure a connector for this edge or inspect connector logs for details." ) logger.error(error_msg) raise RuntimeError(error_msg) logger.debug( "[Orchestrator] Forwarded request %s to stage-%s", req_id, next_stage_id, ) else: logger.debug("[Orchestrator] Request %s fully completed", req_id) logger.debug("[Orchestrator] All requests completed") # Summarize and print stats try: summary = metrics.build_and_log_summary(final_stage_id_for_e2e) logger.info("[Summary] %s", summary) except Exception as e: logger.exception("[Orchestrator] Failed to build/log summary: %s", e) finally: self.request_states.pop(request_id, None) def _wait_for_stages_ready(self, timeout: int = 120) -> None: num_stages = len(self.stage_list) while len(self._stages_ready) < num_stages: progressed = False for stage_id, stage in enumerate(self.stage_list): if stage_id in self._stages_ready: continue result = stage.try_collect() if result is None: continue progressed = True if result.get("type") == "stage_ready": self._stages_ready.add(stage_id) # Store vllm_config received from worker process vllm_config = result.get("vllm_config") if vllm_config is not None: stage.set_vllm_config(vllm_config) tokenizer = result.get("tokenizer") if tokenizer is not None: stage.set_tokenizer(tokenizer) # input_preprocessor = result.get("input_preprocessor") # if input_preprocessor is not None: # stage.set_input_preprocessor(input_preprocessor) is_tracing_enabled = result.get("is_tracing_enabled") if is_tracing_enabled is not None: stage.set_is_tracing_enabled(is_tracing_enabled) logger.debug("[Orchestrator] Stage-%s reported ready", stage_id) else: # No user data should arrive before seeding; ignore other messages pass if not progressed: time.sleep(0.01) if len(self._stages_ready) < num_stages: not_ready = sorted(set(range(num_stages)) - set(self._stages_ready)) logger.warning( "[Orchestrator] Initialization timeout: only %s/%s stages are \ ready; not ready: %s", len(self._stages_ready), num_stages, not_ready, ) # Provide actionable suggestions before shutdown try: suggestions = [ "Verify GPU/device assignment in config (runtime.devices) is \ correct.", "Check GPU/host memory availability; reduce model or batch size if needed.", # noqa: E501 "Check model weights path and network reachability (if loading remotely).", # noqa: E501 "Increase initialization wait time (init_sleep_seconds or \ call-site timeout).", ] if getattr(self, "_log_file", None): suggestions.append( f"Inspect per-stage log files for details: \ {self._log_file}.stage.log" ) logger.error( "[Orchestrator] Stage initialization failed, shutting down. \ Suggestions:\n- %s", "\n- ".join(suggestions), ) except Exception: # Best-effort logging of suggestions logger.error( "[Orchestrator] Stage initialization failed and an error \ occurred while logging suggestions", ) def _run_output_handler(self) -> None: if self.output_handler is not None: return stage_list = self.stage_list request_states = self.request_states async def output_handler(): try: while True: idle = True for stage_id, stage in enumerate(stage_list): result = stage.try_collect() if result is None: continue idle = False if result.get("type") == "stage_ready": # Only happens when stage is initialized slower than expected, # so we wait for a short time and try again await asyncio.sleep(0.05) continue req_id = result.get("request_id") req_state = request_states.get(req_id) if req_state is None: logger.debug( "[Orchestrator] Request may have been aborted; \ dropping output for req %s at stage-%s ", req_id, stage_id, ) continue await req_state.queue.put(result) req_state.stage_id = stage_id if idle: await asyncio.sleep(0.001) # Avoid CPU overload when idle else: await asyncio.sleep(0) except Exception as e: logger.exception("AsyncOmni output_handler failed.") for req_state in request_states.values(): await req_state.queue.put({"request_id": req_id, "error": str(e)}) self.output_handler = None # Make possible for restart self.output_handler = asyncio.create_task(output_handler()) @property def is_running(self) -> bool: # Is None before the loop is started. return len(self._stage_in_queues) > 0 @property def is_stopped(self) -> bool: return self.errored @property def errored(self) -> bool: return not self.is_running @property def dead_error(self) -> BaseException: return EngineDeadError() async def abort(self, request_id: str | Iterable[str]) -> None: pass async def get_vllm_config(self) -> VllmConfig: for stage in self.stage_list: if stage.is_comprehension: # Use the vllm_config received from worker process if stage.vllm_config is not None: return stage.vllm_config return None async def get_model_config(self) -> OmniModelConfig: for stage in self.stage_list: if stage.is_comprehension: # Use the vllm_config received from worker process if stage.vllm_config is not None: return stage.vllm_config.model_config return None async def get_input_preprocessor(self) -> InputPreprocessor: return None async def get_tokenizer(self) -> TokenizerLike: for stage in self.stage_list: if stage.is_comprehension: return stage.tokenizer return None async def is_tracing_enabled(self) -> bool: for stage in self.stage_list: if stage.is_comprehension: return stage.is_tracing_enabled return False async def do_log_stats(self) -> None: pass async def check_health(self) -> None: pass async def reset_mm_cache(self) -> None: pass async def reset_prefix_cache(self, reset_running_requests: bool = False) -> bool: pass async def sleep(self, level: int = 1) -> None: pass async def wake_up(self, tags: list[str] | None = None) -> None: pass async def is_sleeping(self) -> bool: """Check whether the engine is sleeping""" return False async def add_lora(self, lora_request: LoRARequest) -> bool: """Load a new LoRA adapter into the engine for future requests.""" return False async def encode( self, *args, **kwargs, ): """Generate outputs for a request from a pooling model.""" raise NotImplementedError("encode() is not implemented for AsyncOmni") async def start_profile(self) -> None: raise NotImplementedError("start_profile() is not implemented for AsyncOmni") async def stop_profile(self) -> None: raise NotImplementedError("stop_profile() is not implemented for AsyncOmni") async def pause_generation( self, *, wait_for_inflight_requests: bool = False, clear_cache: bool = True, ) -> None: """ Pause generation to allow model weight updates. New generation/encoding requests are blocked until resume. Args: wait_for_inflight_requests: When ``True`` waits for in-flight requests to finish before pausing. When ``False`` (default), immediately aborts any in-flight requests. clear_cache: Whether to clear KV cache and prefix cache after draining. Set to ``False`` to preserve cache for faster resume. Default is ``True`` (clear caches). """ async with self._pause_cond: if self._paused: return self._paused = True # Note: AsyncOmni uses a stage-based architecture without a central # output_processor. For now, we simply set the pause flag and let # new requests wait. In-flight requests will complete naturally. # TODO: Implement request abortion for stages if needed. # Clear cache if requested if clear_cache: await self.reset_prefix_cache() await self.reset_mm_cache() async def resume_generation(self) -> None: """Resume generation after :meth:`pause_generation`.""" async with self._pause_cond: self._paused = False self._pause_cond.notify_all() # Wake up all waiting requests async def is_paused(self) -> bool: """Return whether the engine is currently paused.""" async with self._pause_cond: return self._paused class AsyncOmniStageLLM(AsyncLLM): """Async single-stage LLM engine for use within a stage worker process. This class extends the base vLLM AsyncLLM class with omni-specific processors for handling multimodal inputs and outputs. It is used internally by AsyncOmniStage workers and should not be instantiated directly by users. Args: engine_args: AsyncOmniEngineArgs containing engine configuration vllm_config: Global vLLM configuration executor_class: Executor implementation class, e.g. MultiprocExecutor log_stats: Whether to log statistics usage_context: Usage context of the LLM (default: ENGINE_CONTEXT) mm_registry: Multi-modal registry for processing multimodal inputs use_cached_outputs: Whether to use cached outputs log_requests: Whether to log requests start_engine_loop: Whether to start the engine loop automatically stat_loggers: Customized stat loggers for the engine. If not provided, default stat loggers will be used. Note: Stat logger interface may change in V1. client_addresses: Optional dictionary mapping client names to addresses client_count: Total number of clients (default: 1) client_index: Index of this client (default: 0) """ def __init__( self, engine_args: AsyncOmniEngineArgs, vllm_config: VllmConfig, executor_class: type[Executor], log_stats: bool, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY, use_cached_outputs: bool = False, log_requests: bool = True, start_engine_loop: bool = True, stat_loggers: list[StatLoggerFactory] | None = None, client_addresses: dict[str, str] | None = None, client_count: int = 1, client_index: int = 0, ) -> None: """ Create an AsyncLLM. Args: vllm_config: global configuration. executor_class: an Executor impl, e.g. MultiprocExecutor. log_stats: Whether to log stats. usage_context: Usage context of the LLM. mm_registry: Multi-modal registry. use_cached_outputs: Whether to use cached outputs. log_requests: Whether to log requests. start_engine_loop: Whether to start the engine loop. stat_loggers: customized stat loggers for the engine. If not provided, default stat loggers will be used. PLEASE BE AWARE THAT STAT LOGGER IS NOT STABLE IN V1, AND ITS BASE CLASS INTERFACE MIGHT CHANGE. Returns: None """ # Ensure we can serialize custom transformer configs maybe_register_config_serialize_by_value() self.model_config = vllm_config.model_config self.vllm_config = vllm_config self.observability_config = vllm_config.observability_config self.log_requests = log_requests self.log_stats = log_stats or (stat_loggers is not None) if not log_stats and stat_loggers is not None: logger.info( "AsyncLLM created with log_stats=False and non-empty custom logger list; " "enabling logging without default stat loggers" ) if self.model_config.skip_tokenizer_init: tokenizer = None else: # Tokenizer (+ ensure liveness if running in another process). tokenizer = init_tokenizer_from_config(model_config=vllm_config.model_config) # InputProcessor (converts Inputs --> EngineCoreRequests). self.input_processor = OmniInputProcessor( vllm_config=vllm_config, tokenizer=tokenizer, mm_registry=mm_registry, ) # OutputProcessor (converts EngineCoreOutputs --> RequestOutput). self.output_processor = MultimodalOutputProcessor( tokenizer=tokenizer, log_stats=self.log_stats, engine_core_output_type=engine_args.engine_output_type, ) if self.observability_config.otlp_traces_endpoint is not None: tracer = init_tracer("vllm.llm_engine", self.observability_config.otlp_traces_endpoint) self.output_processor.tracer = tracer # Pause / resume state for async RL workflows. self._pause_cond = asyncio.Condition() self._paused = False # EngineCore (starts the engine in background process). self.engine_core = EngineCoreClient.make_async_mp_client( vllm_config=vllm_config, executor_class=executor_class, log_stats=self.log_stats, client_addresses=client_addresses, client_count=client_count, client_index=client_index, ) # Loggers. self.logger_manager: StatLoggerManager | None = None if self.log_stats: self.logger_manager = StatLoggerManager( vllm_config=vllm_config, engine_idxs=self.engine_core.engine_ranks_managed, custom_stat_loggers=stat_loggers, enable_default_loggers=log_stats, client_count=client_count, ) self.logger_manager.log_engine_initialized() self.output_handler: asyncio.Task | None = None try: # Start output handler eagerly if we are in the asyncio eventloop. asyncio.get_running_loop() self._run_output_handler() except RuntimeError: pass if envs.VLLM_TORCH_PROFILER_DIR: logger.info( "Torch profiler enabled. AsyncLLM CPU traces will be collected under %s", # noqa: E501 envs.VLLM_TORCH_PROFILER_DIR, ) worker_name = f"{socket.gethostname()}_{os.getpid()}.async_llm" self.profiler = torch.profiler.profile( activities=[ torch.profiler.ProfilerActivity.CPU, ], with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK, on_trace_ready=torch.profiler.tensorboard_trace_handler( envs.VLLM_TORCH_PROFILER_DIR, worker_name=worker_name, use_gzip=True ), ) else: self.profiler = None @classmethod @deprecate_kwargs( "disable_log_requests", additional_message=("This argument will have no effect. Use `enable_log_requests` instead."), ) def from_vllm_config( cls, vllm_config: VllmConfig, engine_args: AsyncOmniEngineArgs, start_engine_loop: bool = True, usage_context: UsageContext = UsageContext.ENGINE_CONTEXT, stat_loggers: list[StatLoggerFactory] | None = None, enable_log_requests: bool = False, disable_log_stats: bool = False, client_addresses: dict[str, str] | None = None, client_count: int = 1, client_index: int = 0, disable_log_requests: bool = True, # Deprecated, will be removed ) -> "AsyncLLM": # Create the LLMEngine. return cls( vllm_config=vllm_config, executor_class=Executor.get_class(vllm_config), start_engine_loop=start_engine_loop, stat_loggers=stat_loggers, log_requests=enable_log_requests, log_stats=not disable_log_stats, usage_context=usage_context, client_addresses=client_addresses, client_count=client_count, client_index=client_index, engine_args=engine_args, )